CENTRUM Católica’s Working Paper Series
No. 2016-03-0001 / March 2016
Mining & mitigating social conflicts in Peru
Vincent Charles
CENTRUM Católica Graduate Business School
Pontificia Universidad Católica del Perú
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the author(s).
CENTRUM Católica’s Working Paper No. 2016-03-0001
Mining & mitigating social conflicts in Peru
Vincent Charles
CENTRUM Católica Graduate Business School, PUCP, Lima, Peru
Email: [email protected]
“Reflections from O.R. practice: How soft systems methodology can address wicked environmental
and community issues to keep a key economic engine running.”
Peru is home to an estimated 200 operating mines and related mining projects that could be worth
as much as $59.5 billion [1]. Two fundamental realities should be considered when it comes to the
extractive industry in Peru: On the one hand, there is a great deal of pressure in and on the industry
to get resources out of the ground. On the other hand, the industry is still facing many challenges.
This article depicts the journey undergone in exploring one particular challenge that has been
accompanying almost every new mining report: social conflicts.
Country Snapshot
Peru is located in South America, sharing northern borders with Ecuador and Colombia, an eastern
border with Brazil, a southeastern border with Bolivia, and a southern border with Chile. In the
west it is bordered by the Pacific Ocean. It is formed by 25 regions, which are located on the coast,
the highlands and the jungle.
The Peruvian economy has grown at high rates in recent years, making it one of the best
performing economies in Latin America during that time frame. Peru increased its per capita
income by more than 50 percent over the past 10 years, and the projected rate of economic growth
for 2015 was 2.9 percent. In a recent research study, Peru was classified as the country with the
best “doing business” profile in Latin America [2].
The economic success has been to such extent that it made both academics and practitioners
wonder whether it was indeed an economic miracle or just a mirage – this enquiry led to the
creation of a Harvard Case Study [3], which anyone around the world can analyze and decide what
the truth may be.
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Peruvian Mining Industry and Social Conflicts
For the past three decades, Peru’s model of economic growth has been fueled by the mining
industry, and the expectation is that it continues to be the “engine of growth” of the country.
Mining investment registers remarkable growth, with committed investments for 2011-2016 of
approximately $42.5 billion. Peru holds an estimated 13 percent of the world’s copper reserves, 4
percent of gold, 22 percent of silver, 7.6 percent of zinc, 9 percent of lead and 6 percent of tin.
Peru is the third largest producer of copper, silver and zinc in the world and a major producer of
gold.
The growth of the industry is dependent on the implementation of mining projects in the portfolio,
which has shown significant signs of delay. According to a report issued by McKinsey &
Company published in 2013, 40 percent of the projects in the portfolio have already been affected
and/or delayed due to social issues and conflicts. These conflicts can and have resulted in the
complete breakdown of the impacted companies’ social license to operate. Statistics show that
more than 40 percent of the conflicts involving local communities are about mining, having
increased by 300 percent between 2008 and 2012, with an outcome of 2,312 civilians and police
wounded and 195 killed between 2006 and 2011.
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Figure 1: Considering the great number of mining projects being developed, social conflicts
represent a real challenge to every stakeholder involved. Source: EY Peru (2014) [1].
In the midst of this turmoil, local communities seem to have two major concerns: environmental
degradation (water and land contamination) and lack of benefits to local communities affected by
mining (the lack of improvement of daily life in the form of services, health, education or
infrastructure, despite the enormous wealth generated by the mining industry).
Taming the Wicked Social Conflicts
There is no doubt that the extractive industry can bring significant economic, social and
environmental changes, having the potential to profoundly transform the Peruvian economy and
communities. Throughout the years, many attempts have been made to resolve the conflicts, but
these have generally been piecemeal short-term solutions. In 2012, for example, the government
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approved the Prior Consultation Law, which requires prior consultation with indigenous
communities before any infrastructure or projects, especially mining and energy projects, are
developed in their areas. However, it is this very last law that froze two multibillion-dollar mining
projects: Conga (gold) in Cajamarca region and Tia Maria (copper) in Arequipa region, leading the
president to call a state of emergency.
Some other examples that have made international news are the projects Cerro Quilish in
Cajamarca, Tambogrande and Rio Blanco in Piura and Santa Ana in Puno. The situation of the
mining conflicts is so complex that uncertainty and ambiguity prevail not only over the process,
but over the relationships among the stakeholders, as well. All of the above factors contribute to
the need for a better system for the extractive industry to support the Peruvian economy with
greater concern toward social harmony.
The conflict involves myriad wicked problems – highly complex situations involving several
interested parties with different perspectives over the problem situation in which there is no clear
relationship between cause and effect, and which do not have a unique solution but only better or
worse alternatives. Having no well-defined objectives, traditional mathematical modeling tools of
O.R. become ineffective. However, they may be used at different stages as a means to pinpoint
particular courses of actions to be further explored.
Different Roads to Problem Framing
What follows is a summary of the key features of both the hard and soft O.R. approaches adopted.
The hard side of O.R. has been tackled through optimization and machine learning techniques, and
it was mainly directed toward analyzing the already available data, before embarking on new
avenues of enquiry. The soft side of O.R., as a means to deal further with the messy and
unstructured problems, has been pursued through Soft Systems Methodology (SSM),
complemented by Case-based Reasoning (CBR).
Developing a regional competitiveness index. The journey started in the late 2000s, at a time
when we did not have access to any solid previous research study regarding the situation of the 25
regions of Peru. Ever since and almost every year, CENTRUM Católica Graduate Business School
has been computing the competitiveness of the regions, based on five pillars (economy, firms,
government, infrastructure, persons), factors and variables – in line with the approach of the
International Institute for Management Development (IMD).
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In 2013, we also devised an optimization-based method (Figure 2) to measure the regional
competitiveness of Peru. This work received a “Most Innovative Study” award from Premio Poder
to Peru’s Think Tank of the Year. One of the remarkable insights was that the regions with the
largest mining production in the country, mainly located in the highlands, do not necessarily rank
high in competitiveness. This is known as the “resource curse,” a paradox according to which
regions with an abundance of natural resources, specifically minerals, tend to have less economic
growth.
Figure 2: The structure of the regional competitiveness index.
Source: Charles & Zegarra (2014) [4].
Using machine-learning techniques to study the CSR practices of the mining firms. The general
perception is that the more socially and environmentally responsible a company is perceived to be,
the higher are the chances that the company will not be associated with or involved in social
conflicts. The Corporate Social Responsibility (CSR) reports of the firms, although involving only
documentary evidence, would be a good starting point to have an initial glimpse of such issues.
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But with voluminous CSR reports from multiple firms and spanning many years, analyzing them
manually would become a “near-death experience.” In 2013, and in collaboration with Cornell
University, we proposed an automated way to extract important themes out of the CSR reports
using machine-learning techniques – and we then used these themes to analyze the trends in
various sustainability measures across firms and their alignment to global goals of sustainability.
The major themes of sustainability in the mining industry were formed based on themes previously
identified by the Dow Jones Sustainability Index (DJSI) questionnaire (2009), the Mining and
Metals Supplement of the Global Reporting Initiative (GRI) (2011), the study titled “Sustainability
and Materiality in the Mining Sector” (2011) by Sustainalytics, the Principles of the International
Council on Mining and Metals (ICMM) (2011), and the study “Sustainability in the Mining
Sector” (2007) by FBDS. Preliminary results seem to indicate that firms lack coherence and
discipline in their CSR strategies. We are currently working on intersecting the results with the
knowledge of the experts in the field.
Using SSM to understand the socio-cultural aspects of the mining conflicts. SSM, the most well-
known and widely used soft O.R. methodology, is comprised of several stages, not necessarily
followed in a linear fashion. A rich picture (Figure 3) designed in Stage 1 is aimed at capturing the
main actors and associated issues.
In Stage 2 we defined our root definitions using CATWOE, where:
• C (customers): Local communities, environmentalists
• A (actors): The state, mining companies, other companies
• T (transformation process): Unsatisfied local communities and environmentalists -> satisfied
local communities and environmentalists
• W (worldview): The need for taking effective action to stop mining conflicts and prevent future
conflicts
• O (owner): The state, mining companies
• E (environmental constraints): Environmental degradation, unruly behavior, unbalanced
distribution of mining profits, lack of credibility of the public institutions, illegal mining
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Figure 3: A simplified rich picture capturing the main actors and related concerns in the Peruvian
mining industry.
Based on the above CATWOE, various root definitions were generated, among which the
following excerpt is chosen as an example: “The Peruvian mining industry is a system for ensuring
that local communities feel they have their water and land cared for, are given opportunities for
training and to learn new skills, have access to better services, health, education and infrastructure,
having at the end of the day, a better life.”
In Stage 3, a conceptual model (Figure 4) of the problem situation based on the above root
definition was developed.
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Figure 4: Excerpt from the proposed conceptual model (researchers’ conceptualization).
Stage 4 involved extensive work to compare the conceptual model advanced with what is
perceived to exist in the real world. The realization was that not enough effort has been made in
any area of concern of the local communities – with a fundamental issue residing in deficient
communication and understanding. Furthermore, all the root definitions emphasized the strategic
role played by all of the stakeholders in securing a better life for the local communities affected by
mining, creating the context for social harmony.
But perhaps one of the greatest insights that we have obtained from the analysis performed in
Stage 5 was regarding feasible and desirable changes to be made in the mining industry. One such
insight pointed to the need to dedicate more resources to creating a platform that would allow the
interaction between the various parties involved. The creation of a functional mining cluster as a
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possible change was laid on the table. Our next steps have been directed toward exploring this
path.
Employing case-based reasoning to analyze the international experience. In line with the above
insight regarding the development of a mining cluster in Peru, we have further directed our
attention toward understanding the underpinnings of such an endeavor and alluded to the
international experience. In this regard, we employed CBR, a process of solving new problems
based on the solutions of similar past problems.
CBR involves four steps:
1. Retrieve the most similar cases.
2. Reuse the knowledge obtained from the cases identified to solve our problem.
3. Revise the proposed solution.
4. Retain the lessons learned and use this experience for future problem solving.
The last two steps of the CBR cycle are long-term approaches, and it is more in the hands of the
concerned Peruvian authorities to find the means and to develop a strategy to implement our
propositions.
Developing a mining cluster model for the Peruvian economy. Based on the CBR approach, we
have retrieved both the most similar mining cases and the international stories of success from both
Latin and non-Latin American countries. Hence, step 1 of the CBR process yielded a series of
factors considered critical to the successful development of mining clusters: fiscal stability,
adequate infrastructure, innovation and R&D capacity, skilled workforce and the presence of
foreign investment. The analysis of all of these factors indicated the necessity of both the Peruvian
State and businesses to interact and collaborate with the different academic and research institutes
(the Triple Helix model).
Nevertheless, this result coupled with the insight obtained from Stage 5 of SSM led, in Step 2, to
the need to adapt the Triple Helix and transform it into the Four Clover cluster model (Figure 5). In
this model, a fourth partner, represented by the catalyst institutions (such as technology transfer,
innovation centers and consultancy enterprises), is needed to promote dialogue and cooperation
among the involved parties [5,6]. This solution, although relatively simple, is quite uncommon in
the Peruvian mining sector, as the process is primarily one of learning and negotiation rather than
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the technical solution of a problem. It requires extensive collaboration efforts, and it involves
facilitated workshops of stakeholders that should be used to help them to think through the
consequences of their beliefs, preferences and actions.
Figure 5: From the Triple Helix to the Four Clover Model for the Peruvian mining industry.
Source: Charles (2015) [6].
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Lessons Learned and the Road Ahead
The Peruvian mining industry has the potential to generate significant investments toward
economic development. However, these potentials are undeveloped as social conflicts militate
against them. The research studies conducted so far enabled us to realize that we needed to
intersect soft O.R.-based approaches with hard O.R. methods in order to obtain valid and practical
insights.
In terms of practical implications, our analyses strongly suggest that attempts to understand the
current situation must go as far as including analyses of social interactions on site. Soft O.R.,
complemented by behavioral O.R. as a means to capture human behaviors, can help in this regard.
Concerned parties must understand that the complexity of social conflicts is determined not only
by the variety of stakeholders and the economic-driven relationship existent among them, but also,
to some extent, by socio-cultural aspects.
In many local Peruvian communities, the land is sacred and resettlement of their community for
mining purposes is seen as both an invasion and a sacrilege. Unfortunately, concerned parties seem
to miss its importance. In the messiness of social conflicts, there is some social order, which we
may be able to discover only by personally and intimately immersing in it.
Another example is represented by the sensitive topic of illegal mining (“the hidden dinosaur”),
which is present in 21 of the 25 regions of Peru and that also plays an important part in generating
social conflicts. Besides the need to increase formalization in the sector, there is primarily a need
for a more effective interaction, understanding and collaboration between the parties involved.
Once more, this simply cannot be captured or modeled with mere numerics. Nevertheless, safety
concerns in the Peruvian mining regions emerging from the above-proposed methodological
approach are currently too big and too serious to be ignored.
The journey is long and wicked; nonetheless, progress has been made so far in structuring the
problem and advancing conceptual models and possible solutions. It is our belief that the work
advanced can form the basis for future real-world changes. And our journey continues.
Vincent Charles is the director of research and a distinguished research professor at
CENTRUM Católica Graduate Business School, PUCP, Lima, Peru, where he focuses on the
fields of operations research and analytics. He is the editor-in-chief of JCC: The Business and
Economics Research Journal.
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References
1. EY Peru, 2014. Peru’s mining & metals investment guide 2014/2015. Available at
http://www.ey.com/Publication/vwLUAssets/EY-Peru-mining-metals-investment-guide-14-
15/$FILE/EY-Peru-mining-and-metals-investment-guide-2014-2015.pdf
2. Charles, V., 2015, “Doing Business Across the Continents: A Quick Heads-Up,” STRATEGIA,
Vol. 37, pp. 8-18.
3. Vietor, R. H. K., D’Alessio, F. A., and Pino, R. M., 2015, “Peru: Economic Miracle or Just a
Mirage?” Harvard Business School, Case 9-716-028.
4. Charles, V. and Zegarra, L. F., 2014, “Measuring Regional Competitiveness Through Data
Envelopment Analysis: A Peruvian Case,” Expert Systems with Applications, Vol. 41, No. 11, pp.
5,371-5,381.
5. Charles, V., 2015, “Mining cluster development in Peru: Learning from the International Best
Practice,” Journal of Applied Environmental and Biological Sciences, pp. 1-13.
6. Charles, V., 2015, “Mining Cluster Development in Peru: From Triple Helix to the Four
Clover,” STRATEGIA, Vol. 38, pp. 38-46.